Papers by Yinqiao Li
LoRAN: Improved Low-Rank Adaptation by a Non-Linear Transformation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent methods for fine-tuning large language models have shown great improvements on a wide range of NLP tasks. |
| Approach: | They propose to introduce a non-linear transformation to improve performance of adapters by introducing a low-rank adaptation to fit the accumulated weight updates. |
| Outcome: | The proposed method outperforms a baseline on SAMSum and 20 Newsgroups tasks and even improves the classification task by 1.95 points when a lower rank is applied. |
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods require training millions of architectures to estimate the accuracy of the search results. |
| Approach: | They propose a performance ranking method (RankNAS) that uses pairwise ranking and search space pruning to enlarge the search space. |
| Outcome: | The proposed method significantly accelerates NAS through pairwise ranking and search space pruning. |
Multi-layer Representation Fusion for Neural Machine Translation (C18-1)
Copied to clipboard
| Challenge: | Neural machine translation systems require a number of stacked layers for deep models, but the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. |
| Approach: | They propose a multi-layer representation fusion approach to fusing stacked layers to learn a better representation from the stack. |
| Outcome: | The proposed approach yields 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. |
Learning Architectures from an Extended Search Space for Language Modeling (2020.acl-main)
Copied to clipboard
Yinqiao Li, Chi Hu, Yuhao Zhang, Nuo Xu, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, Changliang Li
| Challenge: | Neural architecture search (NAS) has advanced in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell. |
| Approach: | They propose a general approach to learn both intra-cell and inter-cell architectures . they implement their approach in a differentiable architecture search system . |
| Outcome: | The proposed approach outperforms the baseline on PTB and WikiText data and shows good transferability to other systems. |
Augmenting Large Language Model Translators via Translation Memories (2023.findings-acl)
Copied to clipboard
Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang, Jingbo Zhu
| Challenge: | Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. |
| Approach: | They propose to use translation memories (TMs) as prompts to prompt large language models (LLMs) they find that the ability of LLMs to "understand" prompts is helpful . |
| Outcome: | The results are comparable to state-of-the-art NMT systems with bilingual data and are tuned on downstream tasks. |
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
| Approach: | They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation. |
| Outcome: | The proposed model improves on Chinese-English and English-German translation tasks. |